Data and Models.
We use the publicly available Breast Cancer Wisconsin (Diagnostic) dataset [1]}.
It contains 30 numeric features which we standardized by removing the mean and scaling to unit variance. We explain Scikit-learn [2]}
random forests, trained with a 50% train/test split. Our model consists of ten decision trees of the maximum depth of ten.
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